Lead Cloud Data Engineer

Panera Bread

1d•$127,461 - $163,660•Hybrid

About The Position

Panera Bread is seeking a hands-on Lead Cloud Data Engineer to lead the design, development, delivery, and operational excellence of enterprise data platforms on Google Cloud Platform (GCP). The role bridges enterprise architecture and solution delivery, translating strategic data architecture into scalable, secure, reliable, production-ready solutions for operational and analytical business needs. The engineer provides hands-on development support and technical leadership across the data lifecycle, including ingestion, integration, streaming, transformation, orchestration, governance, retention, and production operations. The position also leads and mentors data engineers, establishes engineering best practices, and ensures performance, scalability, reliability, security, and cost efficiency. The Lead Cloud Data Engineer partners with architects, product owners, business stakeholders, and platform engineering teams to deliver data products and capabilities that support business outcomes. The role promotes architectural consistency, code quality, automation, and operational maturity across the data ecosystem, while evaluating AI, Generative AI, and agentic technologies to improve engineering productivity, automation, data quality, and platform efficiency. This is a hybrid position requiring onsite presence in Newton, Massachusetts, or St. Louis, Missouri. The listed annual salary range is $127,461–$163,660. Actual compensation depends on factors including relevant experience, job-related knowledge, skills, and geographical location. The position has no direct reports, but includes training and mentoring junior engineers and contract workers.

Requirements

Bachelor’s degree in computer science, engineering, MIS, or a related field; a master’s degree is preferred.
At least 5 years of experience in data engineering, data platform development, and cloud-based data solutions, including technical leadership and team mentoring.
At least 5 years of hands-on experience designing, building, and delivering large-scale enterprise data platforms and pipelines on GCP or a similar platform.
At least 4 years of hands-on BigQuery experience, including SQL routines, performance tuning, query optimization, workload management, partitioning, clustering, and materialized views.
At least 4 years of hands-on experience with Dataflow (Apache Beam), Dataproc (Managed Apache Spark), Python, PySpark, SQL, and Pandas for large-scale batch, streaming, and real-time data processing.
Experience managing cloud infrastructure consumption, capacity planning, environment provisioning, and cloud cost optimization, including reservation and usage optimization.
Expertise with Cloud Storage, Cloud SQL, Datastore, DataStream (CDC), Pub/Sub, and event-driven architectures for operational, analytical, real-time, and streaming data integration.
Hands-on experience with Cloud Run, Cloud Functions, Compute Engine, App Engine, and serverless computing architectures for batch, real-time, and streaming data processing.
Advanced experience designing and implementing workflow orchestration and automation using Cloud Composer (Managed Airflow DAG), Cloud Workflows, Cloud Scheduler, and serverless execution frameworks.
Experience implementing Infrastructure as Code, CI/CD pipelines, and automated deployments using Terraform Cloud and GitLab.
Experience implementing data governance, data quality, metadata management, master and reference data management, and Data-as-a-Product principles in enterprise data ecosystems.
Experience with enterprise data ecosystems and technologies including Oracle EDW, PostgreSQL, Kafka, Power BI, and Looker.
Strong analytical, problem-solving, and troubleshooting skills, including resolving complex challenges in large-scale production environments.
Excellent communication and stakeholder management skills, including the ability to influence technical and business teams and explain complex concepts to diverse audiences.
Working knowledge of cloud security, networking, and governance, including IAM, VPC networking, firewall management, observability, logging, DLP, Dataplex (Knowledge Catalog), and enterprise data catalog practices.

Nice To Haves

Master’s degree in computer science, engineering, MIS, or a related field.
Working knowledge of AI and agentic development capabilities, including Vertex AI, Google Gemini Enterprise, Agent Studio, Model Garden, Agent Development Kit (ADK), and MCP integrations.

Responsibilities

Design, develop, test, and deploy large-scale enterprise data engineering pipelines and ETL/ELT data-processing routines on GCP.
Lead, mentor, and develop data engineers, fostering technical excellence, collaboration, accountability, innovation, and continuous learning.
Design, develop, and deploy scalable batch, real-time, and streaming data pipelines aligned with enterprise architecture standards, cloud well-architected principles, and business objectives.
Translate enterprise data architecture patterns into practical, secure, scalable, production-ready solutions for operational, analytical, and data product workloads.
Establish and improve engineering practices, including solution and code reviews, reusable frameworks and patterns, modular development, testing standards, CI/CD, release management, and deployment governance.
Design and implement enterprise workflow orchestration, scheduling, and automation solutions for reliable and resilient data processing.
Define and promote reusable architectural patterns, frameworks, and standards that improve consistency, maintainability, scalability, and operational efficiency.
Ensure solutions adhere to data governance, data quality, security, privacy-by-design, regulatory compliance, and Data-as-a-Product principles.
Evaluate, adopt, and implement emerging technologies that provide business value and improve the data engineering platform.
Lead production operations, including release management, monitoring, observability, incident response, root cause analysis, problem management, and continuous improvement.
Partner with support and operations teams to maintain platform stability, reliability, resiliency, and compliance with change management and operational processes.
Collaborate with architects, product owners, business analysts, BI developers, data engineers, and other stakeholders to align solutions with business priorities, governance requirements, and data product goals.
Optimize platform performance, scalability, reliability, and cost efficiency using cloud-native technologies, automation, and engineering best practices.
Evaluate and apply AI, Generative AI, and agentic AI capabilities to improve engineering productivity, automate operations, improve data quality and cataloging, and accelerate delivery.